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Journal articles on the topic 'OLAP Systems'

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Published: 6 September 2023

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1

Camilleri, Carl, Joseph G. Vella, and Vitezslav Nezval. "HTAP With Reactive Streaming ETL." Journal of Cases on Information Technology 23, no. 4 (October 2021): 1–19. http://dx.doi.org/10.4018/jcit.20211001.oa10.

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In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

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2

Honda, Masayuki, and Takehiro Matsumoto. "System Replacement to a New HIS and Data Warehouse." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 1 (January 20, 2012): 38–41. http://dx.doi.org/10.20965/jaciii.2012.p0038.

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Large-scale hospital information systems (HIS) generally consist of (i) online transaction processing (OLTP) and (ii) online analytical processing (OLAP) systems. Electronic medical records (EMR) are a major OLTP element. The data warehouse (DWH) assumes many important OLAP roles and maintains an institution’s medical care at a high level by providing EMR with the best practice cases available. This article focuses mainly on why OLTP and OLAP are needed and what roles the DWH plays, which means that the DWH has its own utilities and supplementary merits. The background of this discussion is closely related to the HIS at Nagasaki University Hospital introduced before the DWH is discussed.

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3

Torres, Manuel, José Samos, and Eladio Garví. "Closing Ontologies to Define OLAP Systems." International Journal of Information Retrieval Research 4, no. 4 (October 2014): 1–16. http://dx.doi.org/10.4018/ijirr.2014100101.

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Ontologies can be used in the construction of OLAP (On-Line Analytical Processing) systems. In such a context, ontologies are mainly used either to enrich cube dimensions or to define ontology based-dimensions. On the one hand, if dimensions are enriched using large ontologies, like WordNet, details that are beyond the scope of the dimension may be added to it. Even, dimensions may be obscured because of the massive incorporation of related attributes. On the other hand, if ontologies are used to define a dimension, it is possible that a simplified version of the ontology is needed to define the dimension, especially when the used ontology is too complex for the dimension that is being defined. These problems may be solved using one of the existing mechanisms to define ontology views. Therefore, concepts that are not needed for the domain ontology are kept out of the view. However, this view must be closed so that, no ontology component has references to components that are not included in the view. In this work, two basic approaches are proposed: enlargement and reduction closure.

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4

Colliat, George. "OLAP, relational, and multidimensional database systems." ACM SIGMOD Record 25, no. 3 (September 1996): 64–69. http://dx.doi.org/10.1145/234889.234901.

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5

Baltzer, Oliver, Frank Dehne, and Andrew Rau Chaplin. "OLAP for moving object data." International Journal of Intelligent Information and Database Systems 7, no. 1 (2013): 79. http://dx.doi.org/10.1504/ijiids.2013.051745.

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6

Pedersen, Torben Bach, Junmin Gu, Arie Shoshani, and Christian S. Jensen. "Object-extended OLAP querying." Data & Knowledge Engineering 68, no. 5 (May 2009): 453–80. http://dx.doi.org/10.1016/j.datak.2008.10.008.

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7

Dehne, Frank, Todd Eavis, and Boyong Liang. "Compressing Data Cube in Parallel OLAP Systems." Data Science Journal 6 (2007): S184—S197. http://dx.doi.org/10.2481/dsj.6.s184.

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8

SanthoshBaboo, S., and P. Renjith Kumar. "Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database." International Journal of Computer Applications 72, no. 13 (June 26, 2013): 45–50. http://dx.doi.org/10.5120/12557-9282.

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9

Kalnis, Panos, and Dimitris Papadias. "Proxy-server architectures for OLAP." ACM SIGMOD Record 30, no. 2 (June 2001): 367–78. http://dx.doi.org/10.1145/376284.375712.

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10

Sharma, Pitambar, and Piyush Girdhar. "Online Analytical Processing (OLAP)." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (March 31, 2014): 01–04. http://dx.doi.org/10.53555/nncse.v1i3.520.

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This paper is basically accustomed define On-Line Analytical method (OLAP), WHO uses it and why, and to review the key choices required for OLAP code. On-Line Analytical method (OLAP) could also be a category of code technology that allows analysts, managers and executives to appreciate insight into info through fast, consistent, interactive access to an honest reasonably gettable views of {data of information} that has been transformed from data to mirror spatiality of the enterprise as understood by the user. whereas OLAP systems have the ability to answer "who?" and "what?" queries, it's their ability to answer "what if?" and "why?" that sets them except info Warehouses. OLAP applications span a variety of structure functions. Finance departments use OLAP for applications like budgeting, activity-based accountancy (allocations), cash performance analysis, and cash modelling. Sales analysis and prognostication square measure a pair of the OLAP applications found in sales departments. Among totally different applications, promoting departments use OLAP for analysis, sales prognostication, promotions analysis, consumer analysis, and market/customer segmentation. Typical manufacturing OLAP applications embody production coming up with and defect analysis. Although OLAP applications square measure found in wide divergent sensible areas, all of them want the following key choices like 3-D views of information, Calculation –intensive capabilities and Time Intelligence.

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11

Ravat, Franck, Olivier Teste, and Gilles Zurfluh. "Langage pour bases multidimensionnelles : OLAP-SQL." Ingénierie des systèmes d'information 7, no. 3 (June 24, 2002): 11–38. http://dx.doi.org/10.3166/isi.7.3.11-38.

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12

Bimonte, Sandro, Michela Bertolotto, Jérôme Gensel, and Omar Boussaid. "Spatial OLAP and Map Generalization." International Journal of Data Warehousing and Mining 8, no. 1 (January 2012): 24–51. http://dx.doi.org/10.4018/jdwm.2012010102.

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Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP (SOLAP) integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.

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13

Akushko, D. V. "OLAP TECHNOLOGY AS DECISION-MAKING SUPPORT TOOL." Litiyo i Metallurgiya (FOUNDRY PRODUCTION AND METALLURGY), no. 2 (July 4, 2017): 51–53. http://dx.doi.org/10.21122/1683-6065-2017-2-51-53.

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The present article discloses the basic principles of work with OLAP technologies, shows key features in comparison with traditional systems of the reporting. The simplified structure of an OLAP cube for the analysis of cost of products of the steelsmelting shop is considered.

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14

Ho, Ching-Tien, Rakesh Agrawal, Nimrod Megiddo, and Ramakrishnan Srikant. "Range queries in OLAP data cubes." ACM SIGMOD Record 26, no. 2 (June 1997): 73–88. http://dx.doi.org/10.1145/253262.253274.

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15

Dehne, Frank, and Hamidreza Zaboli. "Parallel Real-Time OLAP on Multi-Core Processors." International Journal of Data Warehousing and Mining 11, no. 1 (January 2015): 23–44. http://dx.doi.org/10.4018/ijdwm.2015010102.

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One of the most powerful and prominent technologies for knowledge discovery in decision support systems is online analytical processing (OLAP). Most of the traditional OLAP research, and most of the commercial systems, follow the static data cube approach proposed by Gray et.al. and materialize all or a subset of the cuboids of the data cube in order to ensure adequate query performance. Practitioners have called for some time for a real-time OLAP approach where the OLAP system gets updated instantaneously as new data arrives and always provides an up-to-date data warehouse for the decision support process. However, a major problem for real-time OLAP is the significant performance issues with large scale data warehouses. The aim of our research is to address these problems through the use of efficient parallel computing methods. In this paper, we present a parallel real-time OLAP system for multi-core processors. To our knowledge, this is the first real-time OLAP system that has been parallelized and optimized for contemporary multi-core architectures. Our system allows for multiple insert and multiple query transactions to be executed in parallel and in real-time. We evaluated our method for a multitude of scenarios (different ratios of insert and query transactions, query transactions with different amounts of data aggregation, different database sizes, etc.), using the TPCDS “Decision Support” benchmark data set. As multi-core test platforms, we used an Intel Sandy Bridge processor with 4 cores (8 hardware supported threads) and an Intel Xeon Westmere processor with 20 cores (40 hardware supported threads). The tests demonstrate that, with increasing number of processor cores, our parallel system achieves close to linear speedup in transaction response time and transaction throughput. On the 20 core architecture we achieved, for a 100 GB database, a better than 0.25 second query response time for real-time OLAP queries that aggregate 25% of the database. Since hardware performance improvements are currently, and in the foreseeable future, achieved not by faster processors but by increasing the number of processor cores, our new parallel real-time OLAP method has the potential to enable OLAP systems that operate in real-time on large databases.

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16

Hurtado, Carlos A., Claudio Gutierrez, and Alberto O. Mendelzon. "Capturing summarizability with integrity constraints in OLAP." ACM Transactions on Database Systems 30, no. 3 (September 2005): 854–86. http://dx.doi.org/10.1145/1093382.1093388.

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17

Tremblay, Monica Chiarini, and Alan R. Hevner. "Missing Data in OLAP Cubes." Journal of Database Management 32, no. 3 (July 2021): 1–28. http://dx.doi.org/10.4018/jdm.2021070101.

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Online analytical processing (OLAP) engines display aggregated data to help business analysts compare data, observe trends, and make decisions. Issues of data quality and, in particular, issues with missing data impact the quality of the information. Key decision-makers who rely on these data typically make decisions based on what they assume to be all the available data. The authors investigate three approaches to dealing with missing data: 1) ignore missing data, 2) show missing data explicitly (e.g., as unknown data values), and 3) design mitigation algorithms for missing data (e.g., allocate missing data into known value categories). The authors evaluate the approach with focus groups and controlled experiments. When one tries to inform decision-makers using the approaches in the research, the authors find that they often alter their decisions and adjust their decision confidence: individual differences of tolerance for ambiguity and pre-existing omission bias in the decision context influence their decisions.

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18

Garrigós, Irene, Jesús Pardillo, Jose-Norberto Mazón, Jose Zubcoff, Juan Trujillo, and Rafael Romero. "A Conceptual Modeling Personalization Framework for OLAP." Journal of Database Management 23, no. 4 (October 2012): 1–16. http://dx.doi.org/10.4018/jdm.2012100101.

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OLAP (On-line Analytical Processing) technologies rely on multidimensional models to provide decision makers with appropriate structures allowing them to intuitively analyze data. However, these multidimensional models may be potentially large, thus becoming too complex to be understood at a glance. Current approaches for OLAP design are focused on providing analysts with a single multidimensional schema derived from their previously stated information requirements, but this is not sufficient to lighten the complexity of the decision making process. To overcome this drawback, the authors propose personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behavior. In this paper, they present a new approach for personalizing OLAP systems at the conceptual level based on the underlying multidimensional model, a user model and a set of personalization rules. Transformations are defined by means of a model-driven strategy to assist in the process of obtaining the corresponding personalized OLAP schemas from these models.

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19

Mukasheva, A., D. Yedilkhan, and M. Aldiyar. "MULTIDIMENSIONAL DATABASES IN INFORMATION SYSTEMS OF UNIVERSITIES." Scientific Journal of Astana IT University, no. 9 (March 30, 2022): 85–94. http://dx.doi.org/10.37943/aitu.2022.53.85.008.

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The article is devoted to the description of the method of multidimensional database,which is an effective method of data storage, which allows analyzing data qualitatively, andmost importantly in a short time. The article discusses the capabilities of multidimensionaldatabases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes whenanalyzing large amounts of data. Provides an overview and features of a multidimensionaldatabase and discusses the steps you need to take with a multidimensional database tounderstand the structure and capabilities of an OLAP cube. To create a knowledge base, itdescribes the steps you can take to create and execute a multidimensional database thatyou can collect from various sources, save to a database, and then prepare a report usingOLAP analysis. Various information system data processing technologies such as OLTP andOLAP were considered. The algorithm of the data storage process for analysis purposes wasstudied. A model of a multidimensional database in the form of a three-dimensional cubewas presented. Examples of analysis and ways of obtaining information from the data cubewere also given. The use of a multidimensional database in higher education institutions as asimple and effective method of data storage is considered. There are also illustrations of thestructure of a higher educational institution to see the bulkiness of information, and what kindof information database operates in the educational institution.

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20

Niemi, Tapio, Jyrki Nummenmaa, and Peter Thanisch. "Normalising OLAP cubes for controlling sparsity." Data & Knowledge Engineering 46, no. 3 (September 2003): 317–43. http://dx.doi.org/10.1016/s0169-023x(03)00035-1.

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21

Abello, Alberto, and Il-Yeol Song. "Data warehousing and OLAP (DOLAP’08)." Data & Knowledge Engineering 69, no. 1 (January 2010): 1–2. http://dx.doi.org/10.1016/j.datak.2009.08.011.

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22

Bimonte, Sandro, Omar Boussaid, Michel Schneider, and Fabien Ruelle. "Design and Implementation of Active Stream Data Warehouses." International Journal of Data Warehousing and Mining 15, no. 2 (April 2019): 1–21. http://dx.doi.org/10.4018/ijdwm.2019040101.

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In the era of Big Data, more and more stream data is available. In the same way, Decision Support Systems (DSS) tools, such as data warehouses and alert systems, become more and more sophisticated, and conceptual modeling tools are consequently mandatory for successfully DSS projects. Formalisms such as UML and ER have been widely used in the context of classical information and data warehouse systems, but they have not been investigated yet for stream data warehouses to deal with alert systems. Therefore, in this article, the authors introduce the notion of Active Stream Data Warehouse (ASDW) and this article proposes a UML profile for designing Active Stream Data Warehouses. Indeed, this article extends the ICSOLAP profile to take into account continuous and window OLAP queries. Moreover, this article studies the duality of the stream and OLAP decision-making process and the authors propose a set of ECA rules to automatically trigger OLAP operators. The UML profile is implemented in a new OLAP architecture, and it is validated using an environmental case study concerning the wind monitoring.

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23

Opoku-Anokye, Stephen, and Yinshan Tang. "Design of a Unified Data with Business Rules Storage Model for OLTP and OLAP Systems." Journal of Computing and Information Technology 22, LISS 2013 (2014): 63. http://dx.doi.org/10.2498/cit.1002263.

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24

Shi, Zhongzhi, Youping Huang, Qing He, Lida Xu, Shaohui Liu, Liangxi Qin, Ziyan Jia, Jiayou Li, Huijing Huang, and Lei Zhao. "MSMiner—a developing platform for OLAP." Decision Support Systems 42, no. 4 (January 2007): 2016–28. http://dx.doi.org/10.1016/j.dss.2004.11.006.

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25

Caron, Emiel, and Hennie Daniels. "Explanatory Business Analytics in OLAP." International Journal of Business Intelligence Research 4, no. 3 (July 2013): 67–82. http://dx.doi.org/10.4018/ijbir.2013070105.

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In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP applications with more powerful analysis and reporting functions. The authors describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly, a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data.

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26

Ivanov, S. "Modeling of digital marketing systems construction using olap – technologies." Galic'kij ekonomičnij visnik 72, no. 5 (2021): 85–94. http://dx.doi.org/10.33108/galicianvisnyk_tntu2021.05.085.

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The provision of information to the marketing information system is investigated in this paper. The collection of information on the server side and on network nodes is considered. It is stated that the addition of any information collection software to the server is not possible or can slow down the server. It is proposed to place sensors in network nodes on the approach to the server, which unloads the server from unnecessary software. An example of such Web Traffic Warehouse system is considered. Here the work is carried out at the level of protocols and data collection takes place at the level of TCP/IP packets. Three different approaches to the analysis of transactions are analyzed: identification of transactions taking into account the visits duration, identification of transactions by the method of maximum reference depth and division of transactions according to temporal principles. The scheme of the system of operative processing of Internet data on the basis of OLAP-technologies which is based on the principle of collecting operative data from various sources which then are cleared, integrated and put in relational storage is constructed. Also, the definition of OLAP as a set of tools for multidimensional analysis of data stored in the repository is investigated. It provides the company with the most convenient and fast means of accessing, viewing and analyzing business information, and provides the user with natural, intuitive data model, organizing them into multidimensional cubes, the axes of the multidimensional coordinate system are the main attributes of the analyzed business process. The problem of integration of data warehouses (accounting system) with formation systems (data showcases) is analyzed. Based on the analysis, the model of the distribution-order-sale data show window is built, which is based on two types of tables – fact tables and measurement tables is constructed. Thus, the table of sales facts in the developed system contains such fields as Cusomerfact ID, Salesperson, Product ID, Quantity Sold, Discount, Total Amount, etc. Due to the model it is highlighted that while creating show windows there is the organization of three key stages – obtaining data from the source systems, converting them into the desired form and then loading them into the target system.

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27

Park, Chang-Sup, Myoung Ho Kim, and Yoon-Joon Lee. "Usability-based caching of query results in OLAP systems." Journal of Systems and Software 68, no. 2 (November 2003): 103–19. http://dx.doi.org/10.1016/s0164-1212(02)00142-5.

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28

Chaudhuri, Surajit, and Umeshwar Dayal. "Data warehousing and OLAP for decision support." ACM SIGMOD Record 26, no. 2 (June 1997): 507–8. http://dx.doi.org/10.1145/253262.253373.

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29

Prado, Alysson Bolognesi, Carmen Freitas, and Thiago Ricardo Sbrici. "Using OLAP Tools for e-HRM." International Journal of Technology and Human Interaction 6, no. 4 (October 2010): 49–62. http://dx.doi.org/10.4018/jthi.2010100104.

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In the growing challenge of managing people, Human Resources need effective artifacts to support decision making. On Line Analytical Processing is intended to make business information available for managers, and HR departments can now encompass this technology. This paper describes a project in which the authors built a Data Warehouse containing actual Human Resource data. This paper provides data models and shows their use through OLAP software and their presentation to end-users using a web portal. The authors also discuss the progress, and some obstacles of the project, from the IT staff’s viewpoint.

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30

Burdick, Doug, Prasad M. Deshpande, T. S. Jayram, Raghu Ramakrishnan, and Shivakumar Vaithyanathan. "OLAP over uncertain and imprecise data." VLDB Journal 16, no. 1 (September 29, 2006): 123–44. http://dx.doi.org/10.1007/s00778-006-0033-y.

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31

Khrouf, Omar, Kaïs Khrouf, and Jamel Feji. "OLAP de documents. Modélisation et mise en œuvre." Ingénierie des systèmes d'information 21, no. 1 (February 28, 2016): 11–37. http://dx.doi.org/10.3166/isi.21.1.11-37.

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32

Djedaini, Mahfoud, Krista Drushku, Nicolas Labroche, Patrick Marcel, Verónika Peralta, and Willeme Verdeaux. "Automatic assessment of interactive OLAP explorations." Information Systems 82 (May 2019): 148–63. http://dx.doi.org/10.1016/j.is.2018.06.008.

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33

Oukid, Lamia, Nadjia Benblidia, Fadila Bentayeb, Ounas Asfari, and Omar Boussaid. "Contextualized Text OLAP Based on Information Retrieval." International Journal of Data Warehousing and Mining 11, no. 2 (April 2015): 1–21. http://dx.doi.org/10.4018/ijdwm.2015040101.

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Current data warehousing and On-Line Analytical Processing (OLAP) systems are not yet particularly appropriate for textual data analysis. It is therefore crucial to develop a new data model and an OLAP system to provide the necessary analyses for textual data. To achieve this objective, this paper proposes a new approach based on information retrieval (IR) techniques. Moreover, several contextual factors may significantly affect the information relevant to a decision-maker. Thus, the paper proposes to consider contextual factors in an OLAP system to provide relevant results. It provides a generalized approach for Text OLAP analysis which consists of two parts: The first one is a context-based text cube model, denoted CXT-Cube. It is characterized by several contextual dimensions. Hence, during the OLAP analysis process, CXT-Cube exploits the contextual information in order to better consider the semantics of textual data. Besides, the work associates to CXT-Cube a new text analysis measure based on an OLAP-adapted vector space model and a relevance propagation technique. The second part is an OLAP aggregation operator called ORank (OLAP-Rank) which allows to aggregate textual data in an OLAP environment while considering relevant contextual factors. To consider the user context, this paper proposes a query expansion method based on a decision-maker profile. Based on IR metrics, it evaluates the proposed aggregation operator in different cases using several data analysis queries. The evaluation shows that the precision of the system is significantly better than that of a Text OLAP system based on classical IR. This is due to the consideration of the contextual factors.

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34

Theodoratos, Dimitri, and Aris Tsois. "Processing OLAP queries in hierarchically clustered databases." Data & Knowledge Engineering 45, no. 2 (May 2003): 205–24. http://dx.doi.org/10.1016/s0169-023x(02)00180-5.

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35

Trujillo, Juan, and Il-Yeol Song. "New Trends in Data Warehousing and OLAP." Decision Support Systems 45, no. 1 (April 2008): 1–3. http://dx.doi.org/10.1016/j.dss.2006.12.006.

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36

Colossi, N., W. Malloy, and B. Reinwald. "Relational extensions for OLAP." IBM Systems Journal 41, no. 4 (2002): 714–31. http://dx.doi.org/10.1147/sj.414.0714.

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37

Aligon, Julien, Matteo Golfarelli, Patrick Marcel, Stefano Rizzi, and Elisa Turricchia. "Similarity measures for OLAP sessions." Knowledge and Information Systems 39, no. 2 (March 9, 2013): 463–89. http://dx.doi.org/10.1007/s10115-013-0614-1.

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38

Pourabbas, Elaheh, and Maurizio Rafanelli. "Hierarchies and relative operators in the OLAP environment." ACM SIGMOD Record 29, no. 1 (March 2000): 32–37. http://dx.doi.org/10.1145/344788.344799.

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39

Vassiliadis, Panos, and Timos Sellis. "A survey of logical models for OLAP databases." ACM SIGMOD Record 28, no. 4 (December 1999): 64–69. http://dx.doi.org/10.1145/344816.344869.

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40

Chaudhuri, Surajit, and Umeshwar Dayal. "An overview of data warehousing and OLAP technology." ACM SIGMOD Record 26, no. 1 (March 1997): 65–74. http://dx.doi.org/10.1145/248603.248616.

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41

Nosov, A. P., A. A. Akhrem, and V. Z. Rakhmankulov. "Efficiency Analysis of OLAP-data Hypercube Decomposition for Exponential Computational Complexity Methods." Mathematics and Mathematical Modeling, no. 3 (December 9, 2021): 29–45. http://dx.doi.org/10.24108/mathm.0321.0000258.

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The paper studies problems of reduction (decomposition) of OLAP-hypercube multidimensional data models. When decomposing large hyper-cubes of multidimensional data into sub-cube components the goal is to increase the computational performance of analytical OLAP systems, which is related to decreasing computational complexity of reduction methods for solving OLAP-data analysis problems with respect to the computational complexity of non-reduction methods, applied to data directly all over the hypercube. The paper formalizes the concepts of reduction and non-reduction methods and gives a definition of the upper bound for the change in the computational complexity of reduction methods in the decomposition of the problem of analyzing multidimensional OLAP-data in comparison with non-reduction methods in the class of exponential degree of computational complexity.The exact values of the upper bound for changing computational complexity are obtained for the hypercube decomposition into two sub-cubes on sets consisting of an even and an odd number of sub-cube structures, and its main properties are given, which are used to determine the decomposition efficiency. A formula for the efficiency of decomposition into two sub-cube structures for reduction of OLAP data analysis problems is obtained, and it is shown that with an increase in the dimension “n” of the lattice specifying the number of sub-cubes in the hypercube data structure, the efficiency of such a decomposition obeys an exponential law with an exponent “n/2”, regardless of the parity “n”. The examples show the possibility to use the values (found) of the upper bound for the change in computational complexity to establish the effectiveness criteria for reduction methods and the expediency of decomposition in specific cases.The paper results can be used in processing and analysis of information arrays of hypercube structures of analytical OLAP systems belonging to the Big-Data or super-large computer systems of multidimensional data.

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Boutsinas, B. "On defining OLAP formulations." IMA Journal of Management Mathematics 16, no. 4 (April 12, 2005): 339–54. http://dx.doi.org/10.1093/imaman/dpi012.

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Yatsko, O. M., E. V. Vatamanitsa, and M. P. Gorsky. "Review of olap-modeling application in the economic industry of Ukraine." Optoelectronic Information-Power Technologies 44, no. 2 (January 17, 2023): 5–12. http://dx.doi.org/10.31649/1681-7893-2022-44-2-5-12.

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The analysis of the possibility of using OLAP-modeling that support the activities of financial institutions and support the functioning of business processes, the adoption of effective solutions at all levels of organization management (production, marketing, and personnel decisions, decisions affecting prices, discounts), which in the final the result will lead that the success of the entire organization as a whole, can be implemented through the use of OLAP technologies and OLAP systems. The results of the study can be tested through the implementation of relevant projects driven by challenges and trends in the financial industry, and market and regulatory changes.

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Pourabbas, Elaheh, and Arie Shoshani. "Efficient estimation of joint queries from multiple OLAP databases." ACM Transactions on Database Systems 32, no. 1 (March 2007): 2. http://dx.doi.org/10.1145/1206049.1206051.

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Dehne, F., M. Lawrence, and A. Rau Chaplin. "Cooperative caching for grid-enabled OLAP." International Journal of Grid and Utility Computing 1, no. 2 (2009): 169. http://dx.doi.org/10.1504/ijguc.2009.022032.

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Banerjee, Shreya, Sourabh Bhaskar, Anirban Sarkar, and Narayan C. Debnath. "A Formal OLAP Algebra for NoSQL based Data Warehouses." Annals of Emerging Technologies in Computing 5, no. 5 (March 20, 2021): 154–61. http://dx.doi.org/10.33166/aetic.2021.05.019.

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NoSQL solutions are started to be increasingly used in modern days’ Data Warehouses (DW). However, business analysts face challenges when performing On Line Analytical Processing (OLAP) queries on these NoSQL systems. The lack of uniform representation of various OLAP operations over different types of NoSQL based DWs is one of them. In addition, deficiency of precise semantics in OLAP operations create obstacles to effective query interpretation over distinct types DWs. This paper is aiming to deal with aforementioned challenges. Formal and rigorous specification are represented in this paper for different kinds of OLAP operators and operations. These precise specifications are capable to analyse business queries. Further, the proposed formal specifications are implemented in a document-oriented database using a suitable case study. In addition, the proposed approach aids efficient visualization techniques of data cubes over NoSQL based DWs.

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Layouni, Olfa, and Jalel Akaichi. "Spatio-Temporal OLAP Queries Similarity Measure and Algorithm." International Journal of Data Warehousing and Mining 15, no. 2 (April 2019): 22–41. http://dx.doi.org/10.4018/ijdwm.2019040102.

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Spatio-temporal data warehouses store large volumes of consolidated and historized multidimensional data, to be explored and analyzed by various users in order to make the best decision. A spatio-temporal OLAP user interactively navigates a spatio-temporal data cube (Geo-cube) by launching a sequence of spatio-temporal OLAP queries (GeoMDX queries) in order to analyze the data. One important class of spatio-temporal analysis is computing spatio-temporal queries similarity. In this article, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. The problem of measuring spatio-temporal OLAP queries similarities has not been studied so far. Therefore, this article aims at filling this gap by proposing a new similarity measure and its corresponding algorithm. The proposed measure and algorithm can be used either in developing query recommendation, personalization systems or speeding-up query evolution. It takes into account the temporal similarity and the basic components of spatial similarity assessment relationships.

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Abril Fradel, Diego Orlando, and José Nelson Pérez Castillo. "Current data warehousing and OLAP technologies’ status applied to spatial databases." Ingeniería e Investigación 27, no. 1 (January 1, 2007): 58–67. http://dx.doi.org/10.15446/ing.investig.v27n1.14782.

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Organisations require their information on a timely, dynamic, friendly, centralised and easy-to-access basis for analysing it and taking correct decisions at the right time. Centralisation can be achieved with data warehouse technology. On-line analytical processing (OLAP) is used for analysis. Technologies using graphics and maps in data presentation can be exploited for an overall view of a company and helping to take better decisions. Geographic information systems (GIS) are useful for spatially locating information and representing it using maps. Data warehouses are generally implemented with a multidimensional data model to make OLAP analysis easier. A fundamental point in this model is the definition of measurements and dimensions; geography lies within such dimensions. Many researchers have concluded that the geographic dimension is another attribute for describing data in current analysis systems but without having an in-depth study of its spatial feature and without locating them on a map, like GIS does. Seen this way, interoperability is necessary between GIS and OLAP (called spatial OLAP or SOLAP) and several entities are currently researching this. This document summarises the current status of such research.

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Vaisman, Alejandro A., Alberto O. Mendelzon, Walter Ruaro, and Sergio G. Cymerman. "Supporting dimension updates in an OLAP server." Information Systems 29, no. 2 (April 2004): 165–85. http://dx.doi.org/10.1016/s0306-4379(03)00049-8.

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Mansmann, Svetlana, Nafees Ur Rehman, Andreas Weiler, and Marc H. Scholl. "Discovering OLAP dimensions in semi-structured data." Information Systems 44 (August 2014): 120–33. http://dx.doi.org/10.1016/j.is.2013.09.002.

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